1. The 1st Global Tech Mining Conference, Atlanta, USA Analyzing Technology Evolution of Graphene Sensor Based on Patent Documents Fang Shu 1, Hu Zhengyin 1, Pang Hongshen 1, Zhang Xian 1 1 Chengdu Branch of the National Science Library, Chinese Academy of Sciences, Chengdu, 610041, China

2. OUTLINE  Backgrounds  Methods  Empirical analysis (graphene sensor)  Conclusion and Further Works  Acknowledgement

3. Backgrounds  Our Aims:  Classify the patents by technology evolution trees  Try to find emerging technology  Help to find the important patents

4. Backgrounds  Young’s Work: Young, Jong & Sang (2008) proposed a method of patent analysis for forecasting emerging technology, including:  building a set of patent documents;  extracting technology keywords;  clustering the patent documents;  forming a semantic network of technology keywords;  drawing technology evolution map.

5. Backgrounds  Advantage of Young’s Method  simple operation ;  clear interpretation of the content ;  focusing on technical points ;  reflect the evolution of related technology clearly.

6. Backgrounds  Disadvantage of Young’s Method  suspicion of circular reasoning ；  Ignoring distribution feature and semantic relations of items;  k-Means clustering is not good for small sample.

7. Methods  Our improved method:

8. Methods  Our improved method:  Firstly, build a set of patent documents;  Secondly, extract keywords of technology ;  Thirdly, cluster the patent documents; This is the core improvement.

9. Methods  Our clustering method:  Considering the distribution feature of patent classifications : f ij : the frequency of feature item i appears in the document j; N:number of all documents in the collection; n i : the number of documents including feature item i.

10. Methods  Our clustering method:  Considering the semantic relations between patent classifications: L: the total number of feature items in the document j; θ im : semantic similarity value between feature item i and other feature item m.

11. Methods  Our improved method:  Fourthly, form semantic network of keywords;  Lastly, draw technology evolution map.

12. Empirical analysis  Firstly, build a set of patent documents. Retrieval policy :

13. Empirical analysis  Secondly, extract keywords of technology.(see table 2).

14. Empirical analysis  Thirdly, cluster the patent documents.  Fourthly, form semantic network of keywords.

15. Empirical analysis  Finally, draw technology evolution map.

16. Empirical analysis  Find important patent documents:

17. Empirical analysis  Compared with Young’s method  A semantic network of keywords of graphene sensor (Young’s method)

18. Empirical analysis  Compared with Young’s method  A technology evolution map(Young’s method)

19. Conclusion  Our new method has the following advantages:  Avoiding the defect of circular reasoning;  Considering the distribution features and the semantic features at the same time when clustering;  Using hierarchical clustering which is more suitable for small samples.

20. Further Works  Hope to formulate common standard that helps experts to pick out keywords more accurately ;  Try another methods to build semantic relations or concept hierarchies of terms;  Try to apply the semantic relations of terms for further technology mining.

21. Acknowledgement  Thanks for funding of “Intellectual property rights Information portal of CAS”  Thanks for the experts, including: Prof. Jinhui liu, Prof. Guoshen Chen, Prof. Ge lv,etc.

22. Thank You for the attention!